DocumentCode :
231973
Title :
A multi-feature fusion based traffic light recognition algorithm for intelligent vehicles
Author :
Yue Zhang ; Jianru Xue ; Geng Zhang ; Yingwei Zhang ; Nanning Zheng
Author_Institution :
Visual Cognitive Comput. & Intell. Vehicle Lab., Xi´an Jiaotong Univ., Xi´an, China
fYear :
2014
fDate :
28-30 July 2014
Firstpage :
4924
Lastpage :
4929
Abstract :
Traffic light recognition is a key technology for intelligent vehicles and Advanced Driver Assistance Systems (ADAS). This paper proposes a multi-feature fusion based real-time traffic light recognition algorithm for intelligent vehicles. In the region of interest determined by the vanishing line, technologies including color segmentation, blob detection, and structural feature extraction are employed individually to obtain a set of candidate locations. A fusion algorithm is developed to integrate these results and compute a score for all these possible locations of traffic lights. The score of each candidate denotes its probability of being a traffic light. The final detection is achieved by fusing its score with temporal and geographic information. Extensive experimental results on a real intelligent vehicle show that the proposed algorithm is effective and efficient.
Keywords :
feature extraction; image fusion; image segmentation; intelligent transportation systems; object detection; object recognition; traffic engineering computing; ADAS; advanced driver assistance systems; blob detection; color segmentation; geographic information; intelligent vehicles; multifeature fusion; structural feature extraction; temporal information; traffic light recognition algorithm; vanishing line; Brightness; Cameras; Correlation; Image color analysis; Image segmentation; Intelligent vehicles; Shape; Traffic Light; geographic information; intelligent vehicle; multiple features;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference (CCC), 2014 33rd Chinese
Conference_Location :
Nanjing
Type :
conf
DOI :
10.1109/ChiCC.2014.6895775
Filename :
6895775
Link To Document :
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